DSP-Employee Attrition_ML Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genpact Data Science Prodegree Project Using Python & Machine Learning                                               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitted By: Sagar Gaikwad | Batch: DSP 40\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective of the study:\n",
    "This Project is based on IBM dataset consisting of employee attrition & their retention.This analysis can discover if any particular factors or patterns that lead to attrition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the packagese\n",
    "#Data processing packages\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "#Visualization packages\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "\n",
    "#Machine Learning packages\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "#Suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
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       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
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       "      <td>Life Sciences</td>\n",
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       "      <td>49</td>\n",
       "      <td>No</td>\n",
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       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>80</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "<p>5 rows × 35 columns</p>\n",
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      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import Employee Attrition data\n",
    "df=pd.read_csv(r'attrition.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using EDA we can get data set’s shape, data types of each variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1470, 35)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Age                       1470 non-null   int64 \n",
      " 1   Attrition                 1470 non-null   object\n",
      " 2   BusinessTravel            1470 non-null   object\n",
      " 3   DailyRate                 1470 non-null   int64 \n",
      " 4   Department                1470 non-null   object\n",
      " 5   DistanceFromHome          1470 non-null   int64 \n",
      " 6   Education                 1470 non-null   int64 \n",
      " 7   EducationField            1470 non-null   object\n",
      " 8   EmployeeCount             1470 non-null   int64 \n",
      " 9   EmployeeNumber            1470 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
      " 11  Gender                    1470 non-null   object\n",
      " 12  HourlyRate                1470 non-null   int64 \n",
      " 13  JobInvolvement            1470 non-null   int64 \n",
      " 14  JobLevel                  1470 non-null   int64 \n",
      " 15  JobRole                   1470 non-null   object\n",
      " 16  JobSatisfaction           1470 non-null   int64 \n",
      " 17  MaritalStatus             1470 non-null   object\n",
      " 18  MonthlyIncome             1470 non-null   int64 \n",
      " 19  MonthlyRate               1470 non-null   int64 \n",
      " 20  NumCompaniesWorked        1470 non-null   int64 \n",
      " 21  Over18                    1470 non-null   object\n",
      " 22  OverTime                  1470 non-null   object\n",
      " 23  PercentSalaryHike         1470 non-null   int64 \n",
      " 24  PerformanceRating         1470 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
      " 26  StandardHours             1470 non-null   int64 \n",
      " 27  StockOptionLevel          1470 non-null   int64 \n",
      " 28  TotalWorkingYears         1470 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
      " 30  WorkLifeBalance           1470 non-null   int64 \n",
      " 31  YearsAtCompany            1470 non-null   int64 \n",
      " 32  YearsInCurrentRole        1470 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
      " 34  YearsWithCurrManager      1470 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.1+ KB\n",
      "None\n",
      "                Age Attrition BusinessTravel    DailyRate  \\\n",
      "count   1470.000000      1470           1470  1470.000000   \n",
      "unique          NaN         2              3          NaN   \n",
      "top             NaN        No  Travel_Rarely          NaN   \n",
      "freq            NaN      1233           1043          NaN   \n",
      "mean      36.923810       NaN            NaN   802.485714   \n",
      "std        9.135373       NaN            NaN   403.509100   \n",
      "min       18.000000       NaN            NaN   102.000000   \n",
      "25%       30.000000       NaN            NaN   465.000000   \n",
      "50%       36.000000       NaN            NaN   802.000000   \n",
      "75%       43.000000       NaN            NaN  1157.000000   \n",
      "max       60.000000       NaN            NaN  1499.000000   \n",
      "\n",
      "                    Department  DistanceFromHome    Education EducationField  \\\n",
      "count                     1470       1470.000000  1470.000000           1470   \n",
      "unique                       3               NaN          NaN              6   \n",
      "top     Research & Development               NaN          NaN  Life Sciences   \n",
      "freq                       961               NaN          NaN            606   \n",
      "mean                       NaN          9.192517     2.912925            NaN   \n",
      "std                        NaN          8.106864     1.024165            NaN   \n",
      "min                        NaN          1.000000     1.000000            NaN   \n",
      "25%                        NaN          2.000000     2.000000            NaN   \n",
      "50%                        NaN          7.000000     3.000000            NaN   \n",
      "75%                        NaN         14.000000     4.000000            NaN   \n",
      "max                        NaN         29.000000     5.000000            NaN   \n",
      "\n",
      "        EmployeeCount  EmployeeNumber  ...  RelationshipSatisfaction  \\\n",
      "count          1470.0     1470.000000  ...               1470.000000   \n",
      "unique            NaN             NaN  ...                       NaN   \n",
      "top               NaN             NaN  ...                       NaN   \n",
      "freq              NaN             NaN  ...                       NaN   \n",
      "mean              1.0     1024.865306  ...                  2.712245   \n",
      "std               0.0      602.024335  ...                  1.081209   \n",
      "min               1.0        1.000000  ...                  1.000000   \n",
      "25%               1.0      491.250000  ...                  2.000000   \n",
      "50%               1.0     1020.500000  ...                  3.000000   \n",
      "75%               1.0     1555.750000  ...                  4.000000   \n",
      "max               1.0     2068.000000  ...                  4.000000   \n",
      "\n",
      "       StandardHours  StockOptionLevel  TotalWorkingYears  \\\n",
      "count         1470.0       1470.000000        1470.000000   \n",
      "unique           NaN               NaN                NaN   \n",
      "top              NaN               NaN                NaN   \n",
      "freq             NaN               NaN                NaN   \n",
      "mean            80.0          0.793878          11.279592   \n",
      "std              0.0          0.852077           7.780782   \n",
      "min             80.0          0.000000           0.000000   \n",
      "25%             80.0          0.000000           6.000000   \n",
      "50%             80.0          1.000000          10.000000   \n",
      "75%             80.0          1.000000          15.000000   \n",
      "max             80.0          3.000000          40.000000   \n",
      "\n",
      "        TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
      "count             1470.000000     1470.000000     1470.000000   \n",
      "unique                    NaN             NaN             NaN   \n",
      "top                       NaN             NaN             NaN   \n",
      "freq                      NaN             NaN             NaN   \n",
      "mean                 2.799320        2.761224        7.008163   \n",
      "std                  1.289271        0.706476        6.126525   \n",
      "min                  0.000000        1.000000        0.000000   \n",
      "25%                  2.000000        2.000000        3.000000   \n",
      "50%                  3.000000        3.000000        5.000000   \n",
      "75%                  3.000000        3.000000        9.000000   \n",
      "max                  6.000000        4.000000       40.000000   \n",
      "\n",
      "       YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
      "count         1470.000000              1470.000000           1470.000000  \n",
      "unique                NaN                      NaN                   NaN  \n",
      "top                   NaN                      NaN                   NaN  \n",
      "freq                  NaN                      NaN                   NaN  \n",
      "mean             4.229252                 2.187755              4.123129  \n",
      "std              3.623137                 3.222430              3.568136  \n",
      "min              0.000000                 0.000000              0.000000  \n",
      "25%              2.000000                 0.000000              2.000000  \n",
      "50%              3.000000                 1.000000              3.000000  \n",
      "75%              7.000000                 3.000000              7.000000  \n",
      "max             18.000000                15.000000             17.000000  \n",
      "\n",
      "[11 rows x 35 columns]\n"
     ]
    }
   ],
   "source": [
    "#Exploratory Data Analysis\n",
    "print (df.shape)\n",
    "print (df.info())\n",
    "print (df.describe(include=\"all\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To check unique values present in the data set for each variables and identifying ,any impure value is present   or not in the data set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Age': array([41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 29, 31, 34, 28, 22, 53,\n",
      "       24, 21, 42, 44, 46, 39, 43, 50, 26, 48, 55, 45, 56, 23, 51, 40, 54,\n",
      "       58, 20, 25, 19, 57, 52, 47, 18, 60], dtype=int64)}\n",
      "{'Attrition': array(['Yes', 'No'], dtype=object)}\n",
      "{'BusinessTravel': array(['Travel_Rarely', 'Travel_Frequently', 'Non-Travel'], dtype=object)}\n",
      "{'DailyRate': array([1102,  279, 1373, 1392,  591, 1005, 1324, 1358,  216, 1299,  809,\n",
      "        153,  670, 1346,  103, 1389,  334, 1123, 1219,  371,  673, 1218,\n",
      "        419,  391,  699, 1282, 1125,  691,  477,  705,  924, 1459,  125,\n",
      "        895,  813, 1273,  869,  890,  852, 1141,  464, 1240, 1357,  994,\n",
      "        721, 1360, 1065,  408, 1211, 1229,  626, 1434, 1488, 1097, 1443,\n",
      "        515,  853, 1142,  655, 1115,  427,  653,  989, 1435, 1223,  836,\n",
      "       1195, 1339,  664,  318, 1225, 1328, 1082,  548,  132,  746,  776,\n",
      "        193,  397,  945, 1214,  111,  573, 1153, 1400,  541,  432,  288,\n",
      "        669,  530,  632, 1334,  638, 1093, 1217, 1353,  120,  682,  489,\n",
      "        807,  827,  871,  665, 1040, 1420,  240, 1280,  534, 1456,  658,\n",
      "        142, 1127, 1031, 1189, 1354, 1467,  922,  394, 1312,  750,  441,\n",
      "        684,  249,  841,  147,  528,  594,  470,  957,  542,  802, 1355,\n",
      "       1150, 1329,  959, 1033, 1316,  364,  438,  689,  201, 1427,  857,\n",
      "        933, 1181, 1395,  662, 1436,  194,  967, 1496, 1169, 1145,  630,\n",
      "        303, 1256,  440, 1450, 1452,  465,  702, 1157,  602, 1480, 1268,\n",
      "        713,  134,  526, 1380,  140,  629, 1356,  328, 1084,  931,  692,\n",
      "       1069,  313,  894,  556, 1344,  290,  138,  926, 1261,  472, 1002,\n",
      "        878,  905, 1180,  121, 1136,  635, 1151,  644, 1045,  829, 1242,\n",
      "       1469,  896,  992, 1052, 1147, 1396,  663,  119,  979,  319, 1413,\n",
      "        944, 1323,  532,  818,  854, 1034,  771, 1401, 1431,  976, 1411,\n",
      "       1300,  252, 1327,  832, 1017, 1199,  504,  505,  916, 1247,  685,\n",
      "        269, 1416,  833,  307, 1311,  128,  488,  529, 1210, 1463,  675,\n",
      "       1385, 1403,  452,  666, 1158,  228,  996,  728, 1315,  322, 1479,\n",
      "        797, 1070,  442,  496, 1372,  920,  688, 1449, 1117,  636,  506,\n",
      "        444,  950,  889,  555,  230, 1232,  566, 1302,  812, 1476,  218,\n",
      "       1132, 1105,  906,  849,  390,  106, 1249,  192,  553,  117,  185,\n",
      "       1091,  723, 1220,  588, 1377, 1018, 1275,  798,  672, 1162,  508,\n",
      "       1482,  559,  210,  928, 1001,  549, 1124,  738,  570, 1130, 1192,\n",
      "        343,  144, 1296, 1309,  483,  810,  544, 1062, 1319,  641, 1332,\n",
      "        756,  845,  593, 1171,  350,  921, 1144,  143, 1046,  575,  156,\n",
      "       1283,  755,  304, 1178,  329, 1362, 1371,  202,  253,  164, 1107,\n",
      "        759, 1305,  982,  821, 1381,  480, 1473,  891, 1063,  645, 1490,\n",
      "        317,  422, 1485, 1368, 1448,  296, 1398, 1349,  986, 1099, 1116,\n",
      "       1499,  983, 1009, 1303, 1274, 1277,  587,  413, 1276,  988, 1474,\n",
      "        163,  267,  619,  302,  443,  828,  561,  426,  232, 1306, 1094,\n",
      "        509,  775,  195,  258,  471,  799,  956,  535, 1495,  446, 1245,\n",
      "        703,  823, 1246,  622, 1287,  448,  254, 1365,  538,  525,  558,\n",
      "        782,  362, 1236, 1112,  204, 1343,  604, 1216,  646,  160,  238,\n",
      "       1397,  306,  991,  482, 1176,  913, 1076,  727,  885,  243,  806,\n",
      "        817, 1410, 1207, 1442,  693,  929,  562,  608,  580,  970, 1179,\n",
      "        294,  314,  316,  654,  168,  381,  217,  501,  650,  141,  804,\n",
      "        975, 1090,  346,  430,  268,  167,  621,  527,  883,  954,  310,\n",
      "        719,  725,  715,  657, 1146,  182,  376,  571,  384,  791, 1111,\n",
      "       1243, 1092, 1325,  805,  213,  118,  676, 1252,  286, 1258,  932,\n",
      "       1041,  859,  720,  946, 1184,  436,  589,  760,  887, 1318,  625,\n",
      "        180,  586, 1012,  661,  930,  342, 1230, 1271, 1278,  607,  130,\n",
      "        300,  583, 1418, 1269,  379,  395, 1265, 1222,  341,  868, 1231,\n",
      "        102,  881, 1383, 1075,  374, 1086,  781,  177,  500, 1425, 1454,\n",
      "        617, 1085,  995, 1122,  618,  546,  462, 1198, 1272,  154, 1137,\n",
      "       1188,  188, 1333,  867,  263,  938,  129,  616,  498, 1404, 1053,\n",
      "        289, 1376,  231,  152,  882,  903, 1379,  335,  722,  461,  974,\n",
      "       1126,  840, 1134,  248,  955,  939, 1391, 1206,  287, 1441,  109,\n",
      "       1066,  277,  466, 1055,  265,  135,  247, 1035,  266,  145, 1038,\n",
      "       1234, 1109, 1089,  788,  124,  660, 1186, 1464,  796,  415,  769,\n",
      "       1003, 1366,  330, 1492, 1204,  309, 1330,  469,  697, 1262, 1050,\n",
      "        770,  406,  203, 1308,  984,  439,  793, 1451, 1182,  174,  490,\n",
      "        718,  433,  773,  603,  874,  367,  199,  481,  647, 1384,  902,\n",
      "        819,  862, 1457,  977,  942, 1402, 1421, 1361,  917,  200,  150,\n",
      "        179,  696,  116,  363,  107, 1465,  458, 1212, 1103,  966, 1010,\n",
      "        326, 1098,  969, 1167,  694, 1320,  536,  373,  599,  251,  131,\n",
      "        237, 1429,  648,  735,  531,  429,  968,  879,  640,  412,  848,\n",
      "        360, 1138,  325, 1322,  299, 1030,  634,  524,  256, 1060,  935,\n",
      "        495,  282,  206,  943,  523,  507,  601,  855, 1291, 1405, 1369,\n",
      "        999, 1202,  285,  404,  736, 1498, 1200, 1439,  499,  205,  683,\n",
      "       1462,  949,  652,  332, 1475,  337,  971, 1174,  667,  560,  172,\n",
      "        383, 1255,  359,  401,  377,  592, 1445, 1221,  866,  981,  447,\n",
      "       1326,  748,  990,  405,  115,  790,  830, 1193, 1423,  467,  271,\n",
      "        410, 1083,  516,  224,  136, 1029,  333, 1440,  674, 1342,  898,\n",
      "        824,  492,  598,  740,  888, 1288,  104, 1108,  479, 1351,  474,\n",
      "        437,  884, 1370,  264, 1059,  563,  457, 1313,  241, 1015,  336,\n",
      "       1387,  170,  208,  671,  711,  737, 1470,  365,  763,  567,  486,\n",
      "        772,  301,  311,  584,  880,  392,  148,  708, 1259,  786,  370,\n",
      "        678,  146,  581,  918, 1238,  585,  741,  552,  369,  717,  543,\n",
      "        964,  792,  611,  176,  897,  600, 1054,  428,  181,  211, 1079,\n",
      "        590,  305,  953,  478, 1375,  244,  511, 1294,  196,  734, 1239,\n",
      "       1253, 1128, 1336,  234,  766,  261, 1194,  431,  572, 1422, 1297,\n",
      "        574,  355,  207,  706,  280,  726,  414,  352, 1224,  459, 1254,\n",
      "       1131,  835, 1172, 1266,  783,  219, 1213, 1096, 1251, 1394,  605,\n",
      "       1064, 1337,  937,  157,  754, 1168,  155, 1444,  189,  911, 1321,\n",
      "       1154,  557,  642,  801,  161, 1382, 1037,  105,  582,  704,  345,\n",
      "       1120, 1378,  468,  613, 1023,  628], dtype=int64)}\n",
      "{'Department': array(['Sales', 'Research & Development', 'Human Resources'], dtype=object)}\n",
      "{'DistanceFromHome': array([ 1,  8,  2,  3, 24, 23, 27, 16, 15, 26, 19, 21,  5, 11,  9,  7,  6,\n",
      "       10,  4, 25, 12, 18, 29, 22, 14, 20, 28, 17, 13], dtype=int64)}\n",
      "{'Education': array([2, 1, 4, 3, 5], dtype=int64)}\n",
      "{'EducationField': array(['Life Sciences', 'Other', 'Medical', 'Marketing',\n",
      "       'Technical Degree', 'Human Resources'], dtype=object)}\n",
      "{'EmployeeCount': array([1], dtype=int64)}\n",
      "{'EmployeeNumber': array([   1,    2,    4, ..., 2064, 2065, 2068], dtype=int64)}\n",
      "{'EnvironmentSatisfaction': array([2, 3, 4, 1], dtype=int64)}\n",
      "{'Gender': array(['Female', 'Male'], dtype=object)}\n",
      "{'HourlyRate': array([ 94,  61,  92,  56,  40,  79,  81,  67,  44,  84,  49,  31,  93,\n",
      "        50,  51,  80,  96,  78,  45,  82,  53,  83,  58,  72,  48,  42,\n",
      "        41,  86,  97,  75,  33,  37,  73,  98,  36,  47,  71,  30,  43,\n",
      "        99,  59,  95,  57,  76,  87,  66,  55,  32,  52,  70,  62,  64,\n",
      "        63,  60, 100,  46,  39,  77,  35,  91,  54,  34,  90,  65,  88,\n",
      "        85,  89,  68,  69,  74,  38], dtype=int64)}\n",
      "{'JobInvolvement': array([3, 2, 4, 1], dtype=int64)}\n",
      "{'JobLevel': array([2, 1, 3, 4, 5], dtype=int64)}\n",
      "{'JobRole': array(['Sales Executive', 'Research Scientist', 'Laboratory Technician',\n",
      "       'Manufacturing Director', 'Healthcare Representative', 'Manager',\n",
      "       'Sales Representative', 'Research Director', 'Human Resources'],\n",
      "      dtype=object)}\n",
      "{'JobSatisfaction': array([4, 2, 3, 1], dtype=int64)}\n",
      "{'MaritalStatus': array(['Single', 'Married', 'Divorced'], dtype=object)}\n",
      "{'MonthlyIncome': array([5993, 5130, 2090, ..., 9991, 5390, 4404], dtype=int64)}\n",
      "{'MonthlyRate': array([19479, 24907,  2396, ...,  5174, 13243, 10228], dtype=int64)}\n",
      "{'NumCompaniesWorked': array([8, 1, 6, 9, 0, 4, 5, 2, 7, 3], dtype=int64)}\n",
      "{'Over18': array(['Y'], dtype=object)}\n",
      "{'OverTime': array(['Yes', 'No'], dtype=object)}\n",
      "{'PercentSalaryHike': array([11, 23, 15, 12, 13, 20, 22, 21, 17, 14, 16, 18, 19, 24, 25],\n",
      "      dtype=int64)}\n",
      "{'PerformanceRating': array([3, 4], dtype=int64)}\n",
      "{'RelationshipSatisfaction': array([1, 4, 2, 3], dtype=int64)}\n",
      "{'StandardHours': array([80], dtype=int64)}\n",
      "{'StockOptionLevel': array([0, 1, 3, 2], dtype=int64)}\n",
      "{'TotalWorkingYears': array([ 8, 10,  7,  6, 12,  1, 17,  5,  3, 31, 13,  0, 26, 24, 22,  9, 19,\n",
      "        2, 23, 14, 15,  4, 29, 28, 21, 25, 20, 11, 16, 37, 38, 30, 40, 18,\n",
      "       36, 34, 32, 33, 35, 27], dtype=int64)}\n",
      "{'TrainingTimesLastYear': array([0, 3, 2, 5, 1, 4, 6], dtype=int64)}\n",
      "{'WorkLifeBalance': array([1, 3, 2, 4], dtype=int64)}\n",
      "{'YearsAtCompany': array([ 6, 10,  0,  8,  2,  7,  1,  9,  5,  4, 25,  3, 12, 14, 22, 15, 27,\n",
      "       21, 17, 11, 13, 37, 16, 20, 40, 24, 33, 19, 36, 18, 29, 31, 32, 34,\n",
      "       26, 30, 23], dtype=int64)}\n",
      "{'YearsInCurrentRole': array([ 4,  7,  0,  2,  5,  9,  8,  3,  6, 13,  1, 15, 14, 16, 11, 10, 12,\n",
      "       18, 17], dtype=int64)}\n",
      "{'YearsSinceLastPromotion': array([ 0,  1,  3,  2,  7,  4,  8,  6,  5, 15,  9, 13, 12, 10, 11, 14],\n",
      "      dtype=int64)}\n",
      "{'YearsWithCurrManager': array([ 5,  7,  0,  2,  6,  8,  3, 11, 17,  1,  4, 12,  9, 10, 15, 13, 16,\n",
      "       14], dtype=int64)}\n"
     ]
    }
   ],
   "source": [
    "#Checking unique value in Dataset\n",
    "for i in df.columns:\n",
    "    print({i:df[i].unique()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### verify the missing values present in the data set with the help of isnull().sum() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Attrition                   0\n",
       "BusinessTravel              0\n",
       "DailyRate                   0\n",
       "Department                  0\n",
       "DistanceFromHome            0\n",
       "Education                   0\n",
       "EducationField              0\n",
       "EmployeeCount               0\n",
       "EmployeeNumber              0\n",
       "EnvironmentSatisfaction     0\n",
       "Gender                      0\n",
       "HourlyRate                  0\n",
       "JobInvolvement              0\n",
       "JobLevel                    0\n",
       "JobRole                     0\n",
       "JobSatisfaction             0\n",
       "MaritalStatus               0\n",
       "MonthlyIncome               0\n",
       "MonthlyRate                 0\n",
       "NumCompaniesWorked          0\n",
       "Over18                      0\n",
       "OverTime                    0\n",
       "PercentSalaryHike           0\n",
       "PerformanceRating           0\n",
       "RelationshipSatisfaction    0\n",
       "StandardHours               0\n",
       "StockOptionLevel            0\n",
       "TotalWorkingYears           0\n",
       "TrainingTimesLastYear       0\n",
       "WorkLifeBalance             0\n",
       "YearsAtCompany              0\n",
       "YearsInCurrentRole          0\n",
       "YearsSinceLastPromotion     0\n",
       "YearsWithCurrManager        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\",None)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Fields having common output values or Standard Deviation is zero such columns does not add value. Hence they can be remove using function - drop ( [“ ”] ,axis =1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    1470\n",
       "Name: Over18, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Over18'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of employee attrition in company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels='Attrition Yes','Attrition NO'\n",
    "df['Attrition'].astype(str).value_counts().plot(kind='pie',\n",
    "                                               figsize=(15,6),\n",
    "                                               autopct='%1.1f%%',\n",
    "                                               startangle=90,\n",
    "                                               shadow=True,\n",
    "                                               labels=None)\n",
    "plt.title('Distribution of Employee Attrition in the Company',y=1.12)\n",
    "plt.axis('equal')\n",
    "# add Legend\n",
    "plt.legend(labels=labels,loc='upper right')\n",
    "#show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:-From 1470 employees, 16% of the them left there job & 84% of the employees continued there job at the company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of Rating Features\n",
    "\n",
    "- JobSatisfaction\n",
    "- Environment Satisfaction\n",
    "- Relationship Satisfaction\n",
    "- Job Involvement\n",
    "- Worklife Balance\n",
    "- Performance Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() \n",
    "\n",
    "ax1 = fig.add_subplot(221) \n",
    "ax2 = fig.add_subplot(222)  \n",
    "ax3 = fig.add_subplot(223) \n",
    "ax4 = fig.add_subplot(224)  \n",
    "\n",
    "labels = 'Low','Medium','High','Very High'\n",
    "\n",
    "df['JobSatisfaction'].astype(str).value_counts().plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Rating of Job Satisfaction by Employees')\n",
    "fig.legend(labels=labels,loc='center')\n",
    "\n",
    "df['EnvironmentSatisfaction'].astype(str).value_counts().plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,ax=ax2) \n",
    "ax2.set_title('Rating of Environmental Satisfaction by Employees')\n",
    "\n",
    "df['RelationshipSatisfaction'].astype(str).value_counts().plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,ax=ax3)\n",
    "ax3.set_title('Rating of Relationship Satisfaction by Employees')\n",
    "\n",
    "df['JobInvolvement'].astype(str).value_counts().plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,ax=ax4) \n",
    "ax4.set_title('Rating of Job Involvement by Employees')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 =plt.figure()\n",
    "\n",
    "ax5=fig2.add_subplot(121)\n",
    "ax6=fig2.add_subplot(122)\n",
    "\n",
    "labels_list1='Bad','Good','Better','Best'\n",
    "labels_list2='low','Good','Excellent','Outstanding'\n",
    "\n",
    "df['WorkLifeBalance'].astype(str).value_counts().plot(kind='pie',\n",
    "                                                     figsize=(15,6),\n",
    "                                                     autopct='%1.1f%%',\n",
    "                                                     startangle=90,\n",
    "                                                     shadow=True,\n",
    "                                                     labels=None,ax=ax5)# add to subplot 2\n",
    "ax5.set_title('Rating of  Work-Life Balance by Employees')\n",
    "ax5.legend(labels_list1,loc='upper right')\n",
    "\n",
    "df['PerformanceRating'].astype(str).value_counts().plot(kind='pie',\n",
    "                                                       figsize=(15,6),\n",
    "                                                       autopct=\"%1.1f%%\",\n",
    "                                                       startangle=90,\n",
    "                                                       shadow=True,\n",
    "                                                       labels=None,ax=ax6)\n",
    "ax6.set_title('Performance Rating of the Employees')\n",
    "ax6.legend(labels=labels_list2,loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:-\n",
    "- Almost 60% of the Employees have rated their work-life Balanced as Bad\n",
    "- Almost 85% of the Employees have now performing rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Attrition field has values Yes/No however for Machine learning algorithms we need numeric values.Hence translating Yes/No to binary 1/0 using lambda function.It can take any number of arguments ,but can only have one expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#A lambda function can take any number of arguments, but can only have one expression.\n",
    "df['Attrition']=df['Attrition'].apply(lambda x : 1 if x=='Yes' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For further processing of the data we need to convert categorical values into Numerical values , using pd.get_dummies() function. It can be seen from the difference in the output of data.head() before and after the conversion that now all the fields have numerical values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function is used to convert Categorical values to Numerical values\n",
    "df=pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>JobRole_Manufacturing Director</th>\n",
       "      <th>JobRole_Research Director</th>\n",
       "      <th>JobRole_Research Scientist</th>\n",
       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
       "      <th>MaritalStatus_Divorced</th>\n",
       "      <th>MaritalStatus_Married</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "      <th>OverTime_No</th>\n",
       "      <th>OverTime_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1102</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>279</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1373</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1392</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>591</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Attrition  DailyRate  DistanceFromHome  Education  EmployeeNumber  \\\n",
       "0   41          1       1102                 1          2               1   \n",
       "1   49          0        279                 8          1               2   \n",
       "2   37          1       1373                 2          2               4   \n",
       "3   33          0       1392                 3          4               5   \n",
       "4   27          0        591                 2          1               7   \n",
       "\n",
       "   EnvironmentSatisfaction  HourlyRate  JobInvolvement  JobLevel  ...  \\\n",
       "0                        2          94               3         2  ...   \n",
       "1                        3          61               2         2  ...   \n",
       "2                        4          92               2         1  ...   \n",
       "3                        4          56               3         1  ...   \n",
       "4                        1          40               3         1  ...   \n",
       "\n",
       "   JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               0                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        1   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
       "0                             0                       0   \n",
       "1                             0                       0   \n",
       "2                             0                       0   \n",
       "3                             0                       0   \n",
       "4                             0                       0   \n",
       "\n",
       "   MaritalStatus_Married  MaritalStatus_Single  OverTime_No  OverTime_Yes  \n",
       "0                      0                     1            0             1  \n",
       "1                      1                     0            1             0  \n",
       "2                      0                     1            0             1  \n",
       "3                      1                     0            0             1  \n",
       "4                      1                     0            1             0  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating Feature and Target matrices\n",
    "X = df.drop(['Attrition'], axis=1)\n",
    "Y=df['Attrition']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature scaling is a method used to standardize the range of independent variables or features of data.Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scale = StandardScaler()\n",
    "X = scale.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into Training set and Testing set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size =0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to Train and Test Machine Learning Model\n",
    "\n",
    "def train_test_ml_model(X_train,Y_train,X_test,Model):\n",
    "    model.fit(X_train,Y_train) #Train the Model\n",
    "    Y_pred = model.predict(X_test) #Use the Model for prediction\n",
    "    from sklearn.metrics import classification_report\n",
    "    print(classification_report(Y_test,Y_pred)) \n",
    "\n",
    "    # Test the Model\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    cm = confusion_matrix(Y_test,Y_pred)\n",
    "    accuracy = round(100*np.trace(cm)/np.sum(cm),1) #\n",
    "\n",
    "    #Plot/Display the results\n",
    "    cm_plot(cm,Model)\n",
    "    print('Accuracy of the Model' ,Model, str(accuracy)+'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to plot Confusion Matrix\n",
    "def cm_plot(cm,Model):\n",
    "    plt.clf()\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Wistia)\n",
    "    classNames = ['Negative','Positive']\n",
    "    plt.title('Comparison of Prediction Result for '+ Model)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    tick_marks = np.arange(len(classNames))\n",
    "    plt.xticks(tick_marks, classNames, rotation=45)\n",
    "    plt.yticks(tick_marks, classNames)\n",
    "    s = [['TN','FP'], ['FN', 'TP']]\n",
    "    for i in range(2):\n",
    "        for j in range(2):\n",
    "            plt.text(j,i, str(s[i][j])+\" = \"+str(cm[i][j]))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.95      0.93       255\n",
      "           1       0.58      0.46      0.51        39\n",
      "\n",
      "    accuracy                           0.88       294\n",
      "   macro avg       0.75      0.71      0.72       294\n",
      "weighted avg       0.88      0.88      0.88       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model LogisticRegression 88.4%\n"
     ]
    }
   ],
   "source": [
    "                # LOGOSTIC REGRESSION #\n",
    "    \n",
    "from sklearn.linear_model import LogisticRegression \n",
    "Model = \"LogisticRegression\"\n",
    "model=LogisticRegression()\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As checked in LogisticRegression Model, type 2 error is high. To reduce the same applied Oversampling Technique.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resamplingDataPrep(X_train, Y_train, target_var): \n",
    "    # concatenate our training data back together\n",
    "    resampling = X_train.copy()\n",
    "    resampling[target_var] = Y_train.values\n",
    "    \n",
    "    # separate minority and majority classes\n",
    "    majority_class = resampling[resampling[target_var]==0]\n",
    "    minority_class = resampling[resampling[target_var]==1]\n",
    "    \n",
    "    # Get a class count to understand the class imbalance.\n",
    "    print('majority_class: '+ str(len(majority_class)))\n",
    "    print('minority_class: '+ str(len(minority_class)))\n",
    "    return majority_class, minority_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def upsample_SMOTE(X_train, Y_train, ratio=1.0):\n",
    "    \n",
    "    \"\"\"Upsamples minority class using SMOTE.\n",
    "    Ratio argument is the percentage of the upsampled minority class in relation\n",
    "    to the majority class. Default is 1.0\n",
    "    \"\"\"\n",
    "    sm = SMOTE(random_state=40, sampling_strategy=ratio)\n",
    "    X_train_sm, Y_train_sm = sm.fit_resample(X_train, Y_train)\n",
    "    #print(len(X_train_sm), len(Y_train_sm))\n",
    "    #return X_train_sm, Y_train_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.95      0.93       255\n",
      "           1       0.58      0.46      0.51        39\n",
      "\n",
      "    accuracy                           0.88       294\n",
      "   macro avg       0.75      0.71      0.72       294\n",
      "weighted avg       0.88      0.88      0.88       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model LogisticRegression 88.4%\n"
     ]
    }
   ],
   "source": [
    "                # LOGOSTIC REGRESSION #\n",
    "    \n",
    "from sklearn.linear_model import LogisticRegression \n",
    "Model = \"LogisticRegression\"\n",
    "model=LogisticRegression()\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.94       255\n",
      "           1       0.77      0.26      0.38        39\n",
      "\n",
      "    accuracy                           0.89       294\n",
      "   macro avg       0.83      0.62      0.66       294\n",
      "weighted avg       0.88      0.89      0.87       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model KNeighborsClassifier 89.1%\n"
     ]
    }
   ],
   "source": [
    "                    # KNN #\n",
    "    \n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "Model = \"KNeighborsClassifier\"\n",
    "model=KNeighborsClassifier()\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.91      0.91       255\n",
      "           1       0.38      0.38      0.38        39\n",
      "\n",
      "    accuracy                           0.84       294\n",
      "   macro avg       0.65      0.65      0.65       294\n",
      "weighted avg       0.84      0.84      0.84       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model SGDClassifier 83.7%\n"
     ]
    }
   ],
   "source": [
    "               # SGD #\n",
    "    \n",
    "from sklearn.linear_model import SGDClassifier\n",
    "Model = \"SGDClassifier\"\n",
    "model=SGDClassifier()\n",
    "\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.86      0.87       255\n",
      "           1       0.17      0.18      0.17        39\n",
      "\n",
      "    accuracy                           0.77       294\n",
      "   macro avg       0.52      0.52      0.52       294\n",
      "weighted avg       0.78      0.77      0.78       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model DecisionTreeClassifier 77.2%\n"
     ]
    }
   ],
   "source": [
    "                   # DECISION TREE #\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "Model = \"DecisionTreeClassifier\"\n",
    "model=DecisionTreeClassifier()\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.99      0.93       255\n",
      "           1       0.67      0.10      0.18        39\n",
      "\n",
      "    accuracy                           0.87       294\n",
      "   macro avg       0.77      0.55      0.55       294\n",
      "weighted avg       0.85      0.87      0.83       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model RandomForestClassifier 87.4%\n"
     ]
    }
   ],
   "source": [
    "                   # RANDOM FOREST #\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "Model = \"RandomForestClassifier\"\n",
    "model=RandomForestClassifier()\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94       255\n",
      "           1       0.89      0.21      0.33        39\n",
      "\n",
      "    accuracy                           0.89       294\n",
      "   macro avg       0.89      0.60      0.64       294\n",
      "weighted avg       0.89      0.89      0.86       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model SVC 89.1%\n"
     ]
    }
   ],
   "source": [
    "                 # SUPPORT VECTOR MACHINE #\n",
    "from sklearn.svm import SVC \n",
    "Model = \"SVC\"\n",
    "model=SVC() #Create the Model\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.89      0.90       255\n",
      "           1       0.35      0.38      0.37        39\n",
      "\n",
      "    accuracy                           0.82       294\n",
      "   macro avg       0.63      0.64      0.63       294\n",
      "weighted avg       0.83      0.82      0.83       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model ExtraTreeClassifier 82.3%\n"
     ]
    }
   ],
   "source": [
    "               # EXTRA TREE CLASSIFIER #\n",
    "\n",
    "from sklearn.tree import ExtraTreeClassifier\n",
    "Model = \"ExtraTreeClassifier\"\n",
    "model=ExtraTreeClassifier()\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.95      0.92       255\n",
      "           1       0.46      0.31      0.37        39\n",
      "\n",
      "    accuracy                           0.86       294\n",
      "   macro avg       0.68      0.63      0.65       294\n",
      "weighted avg       0.84      0.86      0.85       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model AdaBoostClassifier 86.1%\n"
     ]
    }
   ],
   "source": [
    "          # AdaBOOST CLASSIFIER #\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "Model = \"AdaBoostClassifier\"\n",
    "model=AdaBoostClassifier()\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.98      0.93       255\n",
      "           1       0.67      0.21      0.31        39\n",
      "\n",
      "    accuracy                           0.88       294\n",
      "   macro avg       0.78      0.59      0.62       294\n",
      "weighted avg       0.86      0.88      0.85       294\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model GradientBoostingClassifier 88.1%\n"
     ]
    }
   ],
   "source": [
    "          # Gradient Boosting Classifier #\n",
    "    \n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "Model = \"GradientBoostingClassifier\"\n",
    "model=GradientBoostingClassifier()\n",
    "\n",
    "train_test_ml_model(X_train,Y_train,X_test,Model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion:-\n",
    "\n",
    "-Performed a set of Classification Algorithms to find the best fit model for our Data.\n",
    "\n",
    "-Logistic Regression:89.1%\n",
    "\n",
    "-KNN Model:85.3%\n",
    "\n",
    "-SGD Model:83.2%\n",
    "\n",
    "-Decision Tree:78.9%\n",
    "\n",
    "-Random forest:86.8%\n",
    "\n",
    "-SVM Model:87.3%\n",
    "\n",
    "-XtraTreeClassifier:78.3%\n",
    "\n",
    "-AdaboostClassifier:87.5 %\n",
    "\n",
    "-Gradient Boosting Classifier:87.8%\n",
    "\n",
    "We conclude that Logistic Regression is the best fit model considering its accuracy is better than other predicted Module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}